{smcl} {* *! version 1.2 March 2021}{...} {cmd: help qregsel} {hline} {title:Title} {phang} {bf:Quantile Regression Corrected for Sample Selection} {title:Syntax} {p 8 17 2} {cmd:qregsel} {it:depvar} {it:varlist} {ifin} {cmd:,} {cmdab:sel:ect(}[{it:depvar_s} {cmd:=}] {it:varlist_s}{cmd:)} {cmd:quantile(}{it:#}{cmd:)} [ {cmd:copula(}{it:copula}{cmd:)} {cmdab:nocons:tant} {cmdab:finergrid} {cmdab:coarsergrid} {cmdab:rescale} {cmdab:nodots} ] {synoptset 20 tabbed}{...} {synopthdr} {synoptline} {syntab:Main} {synopt:{opt sel:ect()}}specifies a selection equation.{p_end} {synopt:{opt quantile:(#)}}specifies the quantiles to be estimated.{p_end} {synopt:{opt copula:(copula)}}specifies a copula; default is gaussian.{p_end} {synopt:{opt nocons:tant}}suppress a constant term in the outcome equation.{p_end} {synopt:{opt finergrid:}}find the value of the copula parameter using a grid of 199 values instead of 100, as done by default.{p_end} {synopt:{opt coarsergrid:}}find the value of the copula parameter using a grid of 50 values instead of 100, as done by default.{p_end} {synopt:{opt rescale:}}rescale the regressors in the outcome equation.{p_end} {synopt:{opt nodots:}}suppress progress dots.{p_end} {title:Description} {pstd} {cmd:qregsel} estimates a copula-based sample selection model for quantile regression. Users can specify a copula from the lists below. {p 4 4 2} Available copulas are {it: gaussian} and {it:frank}. {p 4 4 2} Notes: The name of the copula is case-sensitive. {title:Options} {dlgtab:Main} {phang} {opt sel:ect()} is required. It specifies a selection equation. If {it:depvar_s} is specified, it should be coded as 0 or 1, which 0 indicating {it:depvar} not observed for an observation and 1 indicating {it:depvar} observed for an observation. {phang} {opt quantile(#)} is required. It specifies a set of quantiles to be estimated. {phang} {opt copula(copula)} specifies a copula function for the dependence between outcome and selection equation. See above for the list of available copulas. Default is {bf:gaussian}. {phang} {opt noncons:tant} suppresses a constant term of the outcome equation. {phang} {opt finergrid} find the value of the copula parameter using a grid of 199 values instead of 100, as done by default. These values are chosen such that U and V have a rank correlation between -.99 and .99. {phang} {opt coarsergrid} find the value of the copula parameter using a grid of 50 values instead of 100, as done by default. These values are chosen such that U and V have a rank correlation approximately between -.99 and .97. {phang} {opt rescale} rescale the regressors of the outcome equation substracting sample mean and dividing by standard deviation.. {title:Saved results} {pstd} {cmd:qregsel} saves the following in {cmd:e()}: {synoptset 20 tabbed}{...} {p2col 5 20 24 2: Scalars}{p_end} {synopt:{cmd:e(N)}}number of observations{p_end} {synopt:{cmd:e(N_selected)}}number of selected observations{p_end} {synopt:{cmd:e(rho)}}copula parameter{p_end} {synopt:{cmd:e(kendall)}}Kendall's tau{p_end} {synopt:{cmd:e(spearman)}}Spearman's rank correlation{p_end} {synoptset 20 tabbed}{...} {p2col 5 20 24 2: Macros}{p_end} {synopt:{cmd:e(copula)}}specified {cmd:copula}{p_end} {synopt:{cmd:e(depvar)}}dependent variable{p_end} {synopt:{cmd:e(indepvar)}}independent variables{p_end} {synopt:{cmd:e(cmdline)}}command line{p_end} {synopt:{cmd:e(outcome_eq)}}outcome equation{p_end} {synopt:{cmd:e(select_eq)}}selection equation{p_end} {synopt:{cmd:e(cmd)}}{cmd:qregsel}{p_end} {synopt:{cmd:e(predict)}}predict command name{p_end} {synopt:{cmd:e(rescale)}}use of rescale option{p_end} {synopt:{cmd:e(title)}}title in estimation output{p_end} {synopt:{cmd:e(properties)}}b{p_end} {synoptset 20 tabbed}{...} {p2col 5 20 24 2: Matrices}{p_end} {synopt:{cmd:e(b)}}coefficients matrix{p_end} {synopt:{cmd:e(grid)}}matrix with the values of the objective function for each value of rho, and its respective Spearman rank correlation and Kendall's tau{p_end} {synopt:{cmd:e(coefs)}}coefficient matrix. Each column corresponds to the coefficients for a quantile{p_end} {synoptset 20 tabbed}{...} {p2col 5 20 24 2: Functions}{p_end} {synopt:{cmd:e(sample)}}marks estimation sample{p_end} {p2colreset}{...} {title:Examples} {cmd:. webuse womenwk, clear} {cmd:. qregsel wage educ age, select(married children educ age) quantile(.1 .5 .9)} {cmd:. ereturn list} {title:References} {pstd} Arellano, M., and S. Bonhomme. 2017. "Quantile Selection Models With an Application to Understanding Changes in Wage Inequality." Econometrica 85(1): 1–28. {title:Authors} {p} {p_end} {pstd} Ercio Munoz, CUNY Graduate Center, New York, US. {pstd} Email: {browse "mailto:emunozsaavedra@gc.cuny.edu":emunozsaavedra@gc.cuny.edu} {pstd} Mariel Siravegna, Georgetown University, Washington DC, US. {pstd} Email: {browse "mailto:mcs92@georgetown.edu":mcs92@georgetown.edu} {title: Also see} {psee} Online: {help heckman}, {help qreg}, {help heckmancopula}